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Evolutionary Induction of Classification Trees on Spark

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Artificial Intelligence and Soft Computing (ICAISC 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10841))

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Abstract

Evolutionary-based approaches have recently been increasingly proposed for data mining tasks, but their real applicability depends on efficiency and scalability for large-scale data. It is clear that parallel and distributed processing support is indispensable herein. Apache Spark is one of the most promising cluster-computing engines for Big Data. In this paper, we investigate the application of Spark to speed up an evolutionary induction of classification trees in the Global Decision Tree (GDT) system. The system simultaneously searches for the tree structure and tests in non-terminal nodes due to specialized genetic operators. As the original GDT system is implemented in C++, the Java-based module is developed for Spark-based acceleration of the most computationally demanding fitness evaluation. The training dataset is transformed to Resilient Distributed Dataset, which enables in-memory processing of dataset’s parts on workers. Preliminary experimental validation on large-scale artificial and real-life datasets shows that the proposed solution is efficient and scales well.

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Notes

  1. 1.

    A dipole is a pair of observations; if observations come from different classes, a dipole is called mixed.

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Acknowledgments

This work was supported by the grant S/WI/2/18 from Bialystok University of Technology founded by Polish Ministry of Science and Higher Education.

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Authors and Affiliations

  1. Faculty of Computer Science, Bialystok University of Technology, Wiejska 45a, 15-351, Bialystok, Poland

    Daniel Reska, Krzysztof Jurczuk & Marek Kretowski

Authors
  1. Daniel Reska
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  2. Krzysztof Jurczuk
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  3. Marek Kretowski
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Corresponding author

Correspondence to Krzysztof Jurczuk .

Editor information

Editors and Affiliations

  1. Częstochowa University of Technology, Częstochowa, Poland

    Leszek Rutkowski

  2. Częstochowa University of Technology, Częstochowa, Poland

    Rafał Scherer

  3. Częstochowa University of Technology, Częstochowa, Poland

    Marcin Korytkowski

  4. University of Alberta, Edmonton, AB, Canada

    Witold Pedrycz

  5. AGH University of Science and Technology, Kraków, Poland

    Ryszard Tadeusiewicz

  6. University of Louisville, Louisville, KY, USA

    Jacek M. Zurada

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Reska, D., Jurczuk, K., Kretowski, M. (2018). Evolutionary Induction of Classification Trees on Spark. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J. (eds) Artificial Intelligence and Soft Computing. ICAISC 2018. Lecture Notes in Computer Science(), vol 10841. Springer, Cham. https://doi.org/10.1007/978-3-319-91253-0_48

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  • DOI: https://doi.org/10.1007/978-3-319-91253-0_48

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-91252-3

  • Online ISBN: 978-3-319-91253-0

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